Cross-lingual Sentiment Analysis (CLSA) exploits resources, e. g. labeled data of a high-resource language, to train a sentiment classifier for low-resource languages. This approach is useful when a target language lacks plentiful labeled data, particularly for specific domains. Machine Translation (MT) is often used to bridge the gap between languages [1]}, [2]}, but requires abundant parallel data, which may be difficult to find for some low-resource languages. Approaches that use bilingual distributional representations, in contrast, have proven competitive while requiring less parallel data [3]}, [4]}.
